Triple
T19611080
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Landline series |
E470729
|
entity |
| Predicate | hasNotableWork |
P4
|
FINISHED |
| Object |
Landline Red
Landline Red is a prominent work from the Landline series, known for its bold use of color and minimalist, linear composition.
|
E1387048
|
NE FINISHED |
How this triple was built (4 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Landline Red | Statement: [Landline series, hasNotableWork, Landline Red]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Landline Red Context triple: [Landline series, hasNotableWork, Landline Red]
-
A.
Landline
Landline is a 2017 American comedy-drama film set in 1990s New York City that follows a family dealing with infidelity and personal upheaval, starring Jenny Slate.
-
B.
Coldline
Coldline is a Google Cloud Storage class designed for low-cost, long-term storage of infrequently accessed data with higher retrieval latency and fees.
-
C.
Redline
Redline is a 2007 American action film centered on high-stakes illegal street racing, exotic cars, and underground gambling.
-
D.
Dark Red Line
Dark Red Line is a mass rapid transit route known for its dark red color designation within an MRT system.
-
E.
Baby Bells
The Baby Bells were regional telephone companies created from the 1984 breakup of AT&T’s Bell System, which took over local phone service in different parts of the United States.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Landline Red Triple: [Landline series, hasNotableWork, Landline Red]
Generated description
Landline Red is a prominent work from the Landline series, known for its bold use of color and minimalist, linear composition.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Landline Red Target entity description: Landline Red is a prominent work from the Landline series, known for its bold use of color and minimalist, linear composition.
-
A.
Landline
Landline is a 2017 American comedy-drama film set in 1990s New York City that follows a family dealing with infidelity and personal upheaval, starring Jenny Slate.
-
B.
Coldline
Coldline is a Google Cloud Storage class designed for low-cost, long-term storage of infrequently accessed data with higher retrieval latency and fees.
-
C.
Redline
Redline is a 2007 American action film centered on high-stakes illegal street racing, exotic cars, and underground gambling.
-
D.
Dark Red Line
Dark Red Line is a mass rapid transit route known for its dark red color designation within an MRT system.
-
E.
Baby Bells
The Baby Bells were regional telephone companies created from the 1984 breakup of AT&T’s Bell System, which took over local phone service in different parts of the United States.
- F. None of above. chosen
Provenance (5 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69d8e510fa248190b7afb274a1d4cf73 |
completed | April 10, 2026, 11:54 a.m. |
| NER | Named-entity recognition | batch_69e640cb180c8190ba96ffb69c24e2e1 |
completed | April 20, 2026, 3:05 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0771b2075481908c18e903421703cf |
completed | May 15, 2026, 7:19 p.m. |
| NEDg | Description generation | batch_6a0772b3e6c88190b341479f838093f8 |
completed | May 15, 2026, 7:23 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a077334652c8190b044d444e3004f29 |
completed | May 15, 2026, 7:25 p.m. |
Created at: April 10, 2026, 1:43 p.m.